Method and apparatus to determine electric power network anomalies using a coordinated information exchange among smart meters

ABSTRACT

A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation application of co-pending U.S. patentapplication Ser. No. 14/941,192, filed on Nov. 13, 2015, the entirecontents of which are incorporated by reference herein.

BACKGROUND

Technical Field

The present disclosure relates to electric power networks and, moreparticularly, to smart meters used therein.

Discussion of Related Art

Single-phase power delivery over power lines are used in most homes inNorth America. The single-phase power is able to supply ample power formost smaller customers, including homes and small non-industrialbusinesses and is adequate for running motors up to about 5 horsepower.

Accurate identification of an electric network of low voltage powerdistribution networks is important for reliable operation of electricpower grids. In particular, the integration of renewables and plug-invehicles pushes the demand from customers of the low voltagedistribution networks to their capacity limits.

Smart meters are an advanced energy meter that measures consumption ofelectrical energy providing additional information as compared to aconventional energy meter. Integration of smart meters into anelectricity grid involves implementation of a variety of techniques andsoftware, depending on the features that the situation demands. Designof a smart meter depends on the requirements of the utility company aswell as the customer.

Power-line communication (PLC) utilizes smart meters to provide for thecarrying of data on a conductor that is also used simultaneously foralternating current (AC) electric power transmission or electric powerdistribution to users.

A need exists for smart meters that not only measure electric powercharacteristics but that coordinate information exchange among the smartmeters for the determination of the electric power network anomalies.Exemplary embodiments of the present disclosure provide solutions toneed this need.

BRIEF SUMMARY

Exemplary embodiments of the present disclosure provide a system andmethod to produce an electric network from measurements by smart metersof a temporal delay, an estimated line impedance and physical linelength among smart meter devices.

In accordance with exemplary embodiment of the present disclosure, amethod, system and smart meters used therein provide for obtaining andassessing an electric power network of low voltage distribution grids byexchanging information among smart meters of the electric power networkby: measuring by the smart meters the time-of-travel through the powerlines using an accurate time synchronimzation such as global positioningsatellite (OPS) pulse per second (pps) signals to synchronize time toachieve nano second level synchronization accuracy, and measuring signaldistortion.

Signal distortion may include total harmonic distortion, frequencydistortion, and phase distortion.

Measured time-of-travel may be used to calculate impedance of connectingpower lines by multiplying unit impedance.

Measured time-of-travel and derived matrices therefrom may be used todetect anomalies that include time-of-arrival asymmetry, outliers, andtemporal characteristic changes. Abnormal operating conditions may bedetected by comparing a current measurement matrix with an identifiednormal operating measurement matrix.

Localization of the abnormal operating conditions may be detected bychecking the location in the current measurement matrix by mapping rowand column of the current measurement matrix to a connection between twosmart meters.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments will be more clearly understood from the followingdetailed description taken in conjunction with the accompanying drawingsin which;

FIG. 1 depicts the implementation steps to upgrade an existing smartmeter in accordance with an exemplary embodiment of the presentdisclosure;

FIGS. 2A and 2B depict smart meters in accordance with exemplaryembodiments of the present disclosure;

FIGS. 3A and 3B compare a common modem and a modified common modem usedin smart meters in accordance with exemplary embodiments of the presentdisclosure;

FIGS. 4A and 4B and depict multiple smart meters for line lengthcalculation and electric network creation in accordance with exemplaryembodiments of the present disclosure;

FIG. 5 depicts a chirp packet utilized to send signals along theelectric power line in accordance with an exemplary embodiment of thepresent disclosure;

FIG. 6 sets forth the construction of a characteristic matrix inaccordance with an exemplary embodiment of the present disclosure; and

FIGS. 7A and 7B set forth anomaly testing routines in accordance with anexemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in more detail to the exemplary embodimentswhich are illustrated in the accompanying drawings, wherein likereference numerals refer to the like elements throughout.

Exemplary embodiments of the present disclosure use already existingsmart meters in the field, such as the ITRON® smart meter (ITRON is aregistered trademark of Itron Inc.) which uses the 4G Long-TermEvolution (LTE) GOBI™ embedded mobile broadband modem chipset (GOBI is atrademark of Qualcomm Inc.), which includes GPS capabilities, and uses aGPS pps signal to construct a network topology of smart meters. Thesmart meters of the present disclosure let smart meters coordinate andexchange information (active message passing and sensing) to reconstructan electric network of a low voltage electric grids using the time lagto estimate impedance to help reconstruct the electric network.

Referring to FIG. 1, a depiction of the implementation steps to upgradean existing smart meter is shown. A first Step 1 (102) a new function isadded to the smart meters in an electric network being upgraded so thatthe smart meters can measure time-of-arrival of packets from other smartmeters. A second Step 2 (104) involves undertaking a coordinated packetexchange among the smart meters for Time-of Arrival (TOA) calculation. Athird Step 3 involves the construction of a TOA matrix based upon theTOA calculation and testing for anomalies in the distribution networksusing the TOA matrix.

Referring to FIG. 2A, an exemplary embodiment of smart meter 120includes modified common modem 122 which is connected to single phaseelectric power lines 124 and controlled by microcontroller 126. Inanother exemplary embodiment of smart meter 120, as seen in FIG. 2B, anadditional power amplifier 134 and bandpass filter 136 are included toprovide signal enhancement.

Smart meter 120 is configured to have the ability to: (1) synchronizetime using UPS pps signals, which provides an accurate time stamp; (2)can send/receive an identifiable signal through the same phase ofelectric networks; (3) can identify other smart meters on the same phaselines by listening to the information signal on the same phase lines;and (4) can calculate TOA of an identifiable signal from other smartmeters. The TOA information is used to calculate the line length, whichis then used to calculate impedance of a line and topology of theelectric network. The system then constructs an electric network bycombining geo-spatial information and tree-like usual connectioninformation.

In computer networking, a medium access control (MAC) layer is the lowersublayer of the data link layer (layer 2) of the seven-layer opensystems interconnection (OSI) model. The MAC sublayer providesaddressing and channel access control mechanisms that make it possiblefor several terminals or network nodes to communicate within a networkthat incorporates a shared medium. The MAC sublayer acts as an interfacebetween the logical link control (LLC) sublayer and the networksphysical (PHY) layer in the seven-layer OSI model of computernetworking. The PHY layer consists of the basic networking hardwaretransmission technologies of a network. It is a fundamental layerunderlying the logical data structures of the higher level functions ina network.

Modified common modem 122 is modified to include a PHY layer configuredto allow smart meter 120 the ability to execute the functionality andequations described below utilizing transmitter 128 which receivesPacket Send commands from microcontroller 126, receiver 130 whichreceives Packet Receive commands from microcontroller 126, and GPSmodule 132 which provides Time-Sync data to microcontroller 126.Transmitter 128 and receiver 130 are configured to allow smart meters120 to exchange signals amongst other smart meters 120 coupled to thesame electric power lines 124. GPS module 132 is configured to give aprecise time synchronization amongst other smart meters 120 coupled tothe same electric power lines 124. Smart meters 120 are configured tocalculate the time of arrival of signals by comparing a transmissionstart time and received time wherein the time synchronization allows fora close to exact TOA calculations.

From a hardware perspective, referring to FIGS. 3A and 3B, FIG. 3Adepicts common modem 140 used in current smart meters, while FIG. 3Bdepicts the hardware additions of a bandpass filter and power amplifiersto common modem 142 to allow for improved TOA measurements processing.The remaining modifications to provide for the operation of modifiedcommon modems 122, 142 is in software modifications executable byprocessing hardware.

Referring now to FIGS. 4A and 4B, an operation of plurality of smartmeters 120 a, 120 b, 120, 120 d connected to, and spaced apart on,electric power lines 124, is depicted, wherein a chirp signal sent attime t by smart meter 120 a has a TOA of t+t1 at smart meter 120 b, aTOA at smart meter 120 c of t+t2 and a TOA of at smart meter 120 d oft+t3.

As seen in FIG. 4B, a key factor in establishing the time of arrival ofthe electric signal is due to the group speed of the signal in the lineas in any other communication system.

Referring to FIG. 5, a chirp signal, is a well known signal generationtechnique that has its distinctive signal characteristics. It is asignal in which the frequency increases (‘cup-chirp’) or decreases(‘down-chirp’) with time. A linear chirp waveform provides a sinusoidalwave that increases in frequency linearly over time. A chirp signal canbe generated with analog circuitry voltage controlled oscillator (VCO),and a linearly or exponentially ramping control voltage. FIG. 5. shows achirp packet which is utilized by the smart meters in accordance withexemplary embodiments of the present disclosure. The chip packetincludes a chirp signal, a coded smart meter ID and a coded global timestamp.

Referring back to FIG. 4A, steps to create an electric network inaccordance with an exemplary embodiment of the present disclosureincludes:

1) the farthest end smart meter is chosen.

2) the line length is calculated from the farthest smart meter to theother smart meters.

3) the smart meters are ordered by their distance.

4) the line length between a part of smart meters (e.g., first tosecond, second to third, third to fourth) is calculated.

5) the impedance for each line segment is calculated.

6) an electric line network as impedance is created.

In accordance with FIGS. 6 and 7A, 7B, which respectively depict theconstruction of a characteristic matrix and anomaly testing routines,and equations (28) (36) set forth below:

1) A set of smart meters create a time-of-arrival measurement matrix T.Each element t[i,j] shows the measured time-of-arrival.

2.) A symmetry metric can be tested by checking the temporal delay fromi to j and j to i (first equation).

3) A temporal path anomaly can be tested using the temporal delay from ito j and its velocity c[i,j] and the physical distance between smartmeters d[i,j].

4) An abnormal impedance can be tested if the calculated impedance bycalculating the impedance based on the ‘length of the line’ and the unitimpedance.

5) A consecutive temporal delay can be also used to check the consistenttemporal delays by checking the average temporal delay in the past, andthe current temporal delay. Once the time of arrival matrices areconstructed, the equations below can be used to quantify potentialanomalies.T=[t _(i,j])  (28)where T is a time of travel measurement matrix, t_(i,j) is a time oftravel from i-th smart meter to j-th smart meter, and t_(i,j)=0.Z _(u) =[u _(i,j)]  (29)where is a unit impedance matrix, u_(i,j) is a unit impedance of theline between i-th smart meter to j-th smart meter.C _(u) =[c _(i,j)]  (30)where C_(u) is a group velocity of communication packet, C_(i,j) is agroup velocity of communication packet between i-th smart meter and j-thsmart meter,D=(d _(i,j))  (31)where D is a Euclidean distance matrix between i-th smart meter and j-thsmart meter.Z=[z _(i,j) ]=[u _(i,j) t _(i,j) c _(i,j)]  (32)where Z is an impedance matrix,Testing for symmetry|t _(i,j) −t _(j,i)|□0  (33)Temporal path anomaly|t _(i,j) c _(i,j) −d _(i,j) |≦E  (34)where E is an acceptable discrepancy.Testing for abnormal impedancez _(i,j)□0  (35)where i≈j.Testing for inconsistent time-of-arrivalt _(i,j) □t _(i,j)(k)  (36)where t _(i,j) is a mean time-of arrival and t_(i,j)(k) is a k-th sampleof time-of-arrival between i-th and j-th smart meters.Testing for outlier paths

1. Calculate a normal time-of-arrival/spatial distance t_(i,j)/d_(i,j)

2. Characterize the distribution of t_(i,j)/d_(i,j) where i≠j

3. Identify t_(i,j)/d_(i,j) is not similar to a normal population of{t_(i,j)/d_(i,j)}

The block diagrams in the figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodiments.In this regard, each block in the block diagrams may represent a module,segment, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that, in some alternative implementations by thoseskilled in the art, the functions noted in the block may occur out ofthe order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams, and combinations of blocks in the block diagrams, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

Although illustrative embodiments of the present disclosure have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the present disclosure is not limited to thoseprecise embodiments, and that various other changes and modificationsmay be made therein by those skilled in the art without departing fromthe scope of the claims next presented.

The invention claimed is:
 1. A method for obtaining and assessing anelectric power network of low voltage distribution grids, the methodcomprising: exchanging information among smart meters of the electricpower network by: measuring by the smart meters the time-of-travel ofsignal packets from other smart meters of the electric power networkthrough the power lines using an accurate time synchrony to synchronizetime to achieve nano second level synchronization accuracy, andmeasuring signal distortion, wherein the signal distortion include totalharmonic distortion, frequency distortion, and phase distortion.
 2. Amethod for obtaining and assessing an electric power network of lowvoltage distribution grids, the method comprising: exchanginginformation among smart meters of the electric power network by:measuring by the smart meters the time-of-travel of signal packets fromother smart meters of the electric power network through the power linesusing an accurate time synchrony to synchronize time to achieve nanosecond level synchronization accuracy; and measuring signal distortion,and, detecting abnormal operating conditions by comparing a currentmeasurement matrix with an identified normal operating measurementmatrix.
 3. The method of 2, wherein localization of the abnormaloperating conditions is detected by checking the location in the currentmeasurement matrix by mapping row and column of the current measurementmatrix to a connection between two smart meters.